数字图像分析
人工智能
眼科
图像(数学)
医学
计算机视觉
计算机科学
模式识别(心理学)
验光服务
作者
David A. Merle,Astrid Heidinger,Jutta Horwath‐Winter,Wolfgang List,H. Bauer,Michael Weißensteiner,Patrick Kraus-Füreder,Michael Mayrhofer-Reinhartshuber,Philipp Kainz,Gernot Steinwender,Andreas Wedrich
标识
DOI:10.1080/02713683.2024.2344197
摘要
Purpose Artificial intelligence (AI)-tools hold great potential to compensate for missing resources in health-care systems but often fail to be implemented in clinical routine. Intriguingly, no-code and low-code technologies allow clinicians to develop Artificial intelligence (AI)-tools without requiring in-depth programming knowledge. Clinician-driven projects allow to adequately identify and address real clinical needs and, therefore, hold superior potential for clinical implementation. In this light, this study aimed for the clinician-driven development of a tool capable of measuring corneal lesions relative to total corneal surface area and eliminating inaccuracies in two-dimensional measurements by three-dimensional fitting of the corneal surface.
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